Detecting Upscaled Source Video

ABSTRACT

Techniques are disclosed for estimating a source resolution of image data presented to a system. According to these techniques, input image data may be converted to a domain of frequency coefficients. Each coefficient may represent content of the input image along a respective pair of frequencies extending in two dimensions. For each set of frequency coefficients having a common frequency in one of the dimensions, zero crossings of coefficient values may be detected. The zero crossings may be counted at each frequency position in the second dimension. An estimate of the input image&#39;s source resolution may be estimated from a comparison of the zero crossings. For video, this process may be performed across images of an input video sequences.

BACKGROUND

The present disclosure relates to media delivery systems and, inparticular, to techniques for estimating source resolution of mediaitems that are candidates for distribution.

There are many applications for media distribution in modern commerce.Although applications vary widely, media delivery systems often cause amedia item having video or audio/visual content to be delivered from afirst networked device (a “distribution server,” for convenience) to asecond networked device (a “client”), where it is rendered. Renderingmay occur on personal computing devices, for example, personalcomputers, tablet computers, smartphones and/or personal media players,or it may occur on dedicated media players, such as televisions and/ortheater systems. Moreover, the format of the media items may varywidely. The media items may be provided as 720p video, 1080p video, 4Kvideo or any of a variety of different representations. In many cases, adistribution server may possess several copies of a single media item,each at different representations (e.g., 720p, 1080p, 4K, etc.), and itmay operate according to policies that attempt to guarantee that thedifferent representations actually meet the quality standards that areattendant to them.

A distribution server may not create the media items that it stores inall cases and, therefore, a proprietor of the distribution server cannotguarantee that a given instance of a media item meets the qualityrequirements of its associated representation. For example, an instanceof a media item may have been uploaded to the distribution server in afirst format even though it initially was created in a second,lower-resolution format. Prior to upload, the media item may have beenupsampled, converted from a native, lower resolution format to a higherresolution. The upsampled image would be considered to have lowerquality than an image that is natively at the higher resolution becausethe additional pixels in the upsampled image do not contain any detailthat was not expressed at the lower resolution.

The inventors, therefore, have identified a need in the art for a toolto analyze a media item and determine whether a media item that ispresented was created in at least the resolution in which it ispresented.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a media delivery system according to an embodiment ofthe present disclosure.

FIGS. 2(a)-2(g) illustrates exemplary interpolation processes.Specifically, FIG. 2(b) illustrates exemplary source data that may beinput to an interpolation system as illustrated in FIG. 2(a). FIGS.2(c)-(g) respectively illustrate effects of different interpolationprocesses.

FIG. 3 illustrates a method according to an embodiment of the presentdisclosure.

FIGS. 4(a)-4(c) illustrates an exemplary source image (FIG. 4(a)) thatmay be subject to upsampling. FIG. 4(b) illustrates an exemplaryfrequency transform of the source image of FIG. 4(a) after having beenupsampled. FIG. 4(c) illustrates an exemplary frequency transform of thesource image of FIG. 4(a) without upsampling.

FIGS. 5(a)-5(d) illustrates exemplary graphs of coefficient values forthree row of a transformed image, shown in FIGS. 5(a)-(c), and anexemplary summation of zero crossings, shown in FIG. 5(d).

FIG. 6 illustrates a method of estimating source resolution of a videosequence according to an embodiment of the present disclosure.

FIG. 7 illustrates an exemplary computer system suitable for use withembodiments of the present disclosure.

DETAILED DESCRIPTION

Embodiments of the invention provide techniques for estimating a sourceresolution of image data presented to a system. According to thesetechniques, input image data may be converted to a domain of frequencycoefficients. Each coefficient may represent content of the input imagealong a respective pair of frequencies extending in two dimensions. Foreach set of frequency coefficients having a common frequency in one ofthe dimensions, zero crossings of coefficient values may be detected.The zero crossings may be counted at each frequency position in thesecond dimension. An estimate of the input image's source resolution maybe estimated from a comparison of the zero crossings. For video, thisprocess may be performed across images of an input video sequences.

FIG. 1 illustrates a media delivery system 100 according to anembodiment of the present disclosure. The system 100 may include one ormore client terminals 110 provided in communication with a distributionserver 120 via a network 130. The distribution server 120 may storevarious media items 140.1-140.N in local storage 125. The distributionserver 120 may deliver a media item (say, item 140.1) to a clientterminal 110 on request.

Media items 140.1-140.N may be provided to the distribution server 120from a variety of sources. In one example, the distribution server 120may be operated by a commercial enterprise that provides commercialguarantees regarding the media items 140.1-140.N that it furnishes tothe client terminals 110. For example, the enterprise may indicate thatthe media items are provided at predetermined resolution levels, forexample, at 4K resolution, 1080p resolution, 720p resolution and thelike. If the enterprise furnishes a media item 140.1 that is representedto be at a first video resolution (for example, 4K resolution) but itactually possesses an inferior resolution due to upsampling, then theenterprise would violate its own policies.

The distribution server 120 may not create the media items 140.1-140.Nthat it stores. In some applications, media items may be furnished tothe distribution sever 120 from sources 150 (called “authoring sources,”for convenience) that the distribution server 120 does not control. And,while the distribution server 120 may perform operations to confirm thata given media item is provided in a format that satisfies itsrepresentations (e.g., a 4K resolution media item matches a file formatthat corresponds to 4K video), it is possible that a media item 140.1will have been altered from a lower-resolution representation of videoto a higher-resolution representation.

FIG. 2(a)-(g) illustrate types of interpolation that may be performedwhen upsampling a source image. A source image may be input to aninterpolation filter that generates an image, which may be furnished tothe distribution server (FIG. 1) as an input image, having higherresolution. Interpolation may occur according to any of a number ofdifferent techniques. For example, FIGS. 2(c)-(g) illustrateinterpolation that may occur along a single axis (either a row or acolumn) of image data given a source set of pixel values, shown in FIG.2(b). Interpolation may occur by nearest value interpolation (FIG.2(c)), bilinear interpolation (FIG. 2(d)), bicubic interpolation (FIG.2(e)), Gaussian interpolation (FIG. 2(f)) or Lanczos interpolation (FIG.2(g)), among others. Each technique has its own level of complexity andgenerates its own set of image artifacts in the upsampled image. Asdescribed, at the end of the upsampling process, a resultant image mayhave a higher pixel resolution than the source image but the increasedpixel resolution does not improve information content of the image.

FIG. 3 illustrates a method 300 according to an embodiment of thepresent disclosure. The method 300 may perform a frequency transform ofan input image (box 310). Thus, where an input image constitutes aspatial array of pixel values, the frequency transform may generate aspatial array of frequency coefficients, where each coefficientrepresents a predetermined component of the original image at a pair offrequencies each extending in a respective direction (e.g., a firstfrequency in a horizontal direction in the image and a second frequencyin a vertical direction). The transform coefficients may be arranged incolumnar and row positions according to the frequencies they represent.Thereafter, the method 300 may process each row and each column of thetransformed image.

The method 300 may traverse each row of the transformed image andidentify columnar positions on each row that represent zero crossings ofcoefficient values (box 315). After the zero crossings on each row areidentified, the method 300 may count, across all rows of the transformedimage, the number of zero crossings at each columnar position (box 320).The method 300 may determine whether there are columnar positions thathave a large number of zero crossings associated with them (box 325). Ifso, then the method 300 may estimate the native width of a source imagefrom the columnar position(s) with the most significant number of zerocrossings (box 330). If not, then no conclusions about the native widthof the source image will be drawn (box 335).

The method 300 also may traverse each column of the transformed imageand identify row positions on each column that represent zero crossingsof coefficient values (box 340). After the zero crossings on each columnare identified, the method 300 may count, across all columns of thetransformed image, the number of zero crossings at each row position(box 345). The method 300 may determine whether there are row positionsthat have a large number of zero crossings associated with them (box350). If so, then the method 300 may estimate the native height of asource image from the row position(s) with the most significant numberof zero crossings (box 355). If not, then no conclusions about thenative height of the source image will be drawn (box 360).

FIGS. 4 (a)-(c) illustrate application of the method 300 of FIG. 3 to anexemplary source image. FIG. 4(a) illustrates an exemplary source image410 that may be upsampled prior to being submitted to a mediadistribution system. In its native resolution, the source image may havea first resolution, say 512×512 pixels, but it may be upsampled to adifferent resolution, say 1024×1024 pixels (image not shown), beforebeing input to the media distribution system. For discussion purposes,it may be assumed that the upsampling is performed according to bilinearinterpolation.

FIG. 4(b) illustrates a plot of a frequency transform of the image ofFIG. 4(a) after being upsampled to a higher resolution. In the exampleof FIG. 4(b), the transform may create a 1024×1024 array of frequencycoefficients. Typically, an origin of the array may carry a coefficientcorresponding to the lowest frequency in the array (a DC coefficient).At different positions along a given row of the array, the coefficientsrepresent increasing frequency component in the columnar direction. Atdifferent positions along a given column of the array, the coefficientsrepresent increasing frequency component in the row direction. In thegrayscale illustration of FIGS. 4(a)-4(c), white content representscoefficients having relatively large magnitudes and darker contentrepresents coefficient values having relatively small magnitudes. Thecoefficients also may have a sign component (e.g., they are eitherpositive or negative) but these components are not illustrated in FIG.4(b).

When an input image has been generated from upsampling of a source imageat a lower resolution, the frequency transform of the input image tendsto exhibit zero valued coefficients at frequencies that correspond tothe degree of upsampling. For example, as illustrated in FIG. 4(b), theinput image exhibits a row 421 and a column 422 whose frequencycoefficients are essentially zero-valued. The method 300 essentiallysearches for these small coefficient values in its row-by-row andcolumn-by-column searches.

FIG. 4(c) illustrates a frequency transform of the source image 410 atits native resolution. In this example, a 512×512 pixel image istransformed to a 512×512 array of transform coefficients. The frequencytransform 430 of FIG. 4(c) does not exhibit the zero-valued coefficientsthat are found in the frequency transform 420 of the upsampled versionof the source image 410.

FIG. 5(a)-5(d) illustrates exemplary graphs of coefficient values forthree rows of a transformed image. In this example, a first row (Row 1)is shown as having three zero crossings 502-506 at various columnarpositions along the row, a second row (Row 2) is shown having five zerocrossings 508-516 at various columnar positions along the second row,and a third row (Row 3) is also shown having five zero crossings 518-526at various positions along that row.

In this example, the zero crossings 504, 512 and 522 of the three rowscoincide at a common columnar position. The other zero crossings 502,506-510, 514-520 and 524-526 do not coincide with each other. Thus, inthis example, when the method 300 calculates the number of zerocrossings at each columnar position, the position correspond to the zerocrossings 506, 516 and 528 have a higher zero crossing count value thanthe positions of the other zero crossings 502, 506-510, 514-520 and524-526. And, when the count values are summed across all 1,024 rows ofthe transform array of FIG. 4(b), count values might occur as shown inFIG. 5(d). In this example, a large count value is observed at acolumnar position mid-way across the rows (position 512 in a row having1,024 coefficients), which indicates that the source image's nativewidth was 512 pixels.

A similar phenomenon may be observed with zero crossings that occur incolumns of the transform array. It is expected that, when zero crossingcount values are summed across all columns of a transform array and alarge count value is observed at row position(s) along the columns, itindicates the source image's native height.

As shown above, the method 300 of FIG. 3 may estimate the sourceresolution of an input image.

In many applications, images that have been upsampled exhibit certainpatterns when converted in the frequency domain. For example, asillustrated in FIGS. 5(a)-5(d), upsampled images often exhibit patternsin frequency distribution that, absent noise or some other distortion,cause frequency coefficients on one side of a zero crossing to bemirrored on an opposite side of the zero crossing. Consider thecoefficients illustrated in FIGS. 5(a)-(c). Frequency coefficients aremirrored on opposite sides of the zero crossings 504, 512 and 522,respectively, whereas frequencies coefficients are not mirrored in thecases of zero crossings 502, 506-510, 514-520 and 524-526. In anembodiment, the method 300 may analyze the frequency coefficients at aplurality of distances on one side of a zero crossing and compare themto counterpart frequency coefficients at the same distance on the otherside of the zero crossing. If the magnitudes of the frequencycoefficients match those of their counterparts, the candidate zerocrossing may be given a higher weight in summation than another zerocrossing where frequency coefficients on one side of the other zerocrossing do not match those of their counterparts on the other side ofthe other zero crossing.

In another application, upsampling may cause frequency coefficients tochange sign at a zero crossing. In such an embodiment, the method 300may analyze the frequency coefficients at a plurality of distances onone side of a zero crossing and compare them to counterpart frequencycoefficients at the same distance on the other side of the zerocrossing. If the signs of the frequency coefficients differ from thoseof their counterparts, the candidate zero crossing may be given a higherweight in summation than another zero crossing where frequencycoefficients on one side of the other zero crossing do not match thoseof their counterparts on the other side of the other zero crossing. In afurther embodiment, if the signs of the coefficients on either side of azero crossing match each other, the method 300 may sum up the magnitudesof the coefficients on either side of the zero crossing. If the summedmagnitudes match each other, the increased weight may be given to thecandidate zero crossing.

In a further embodiment, candidate zero crossings may be removed fromconsideration (or given relatively small weights) when they aresurrounded by frequency coefficients below a given magnitude.

As indicated, the count of zero crossings contemplated by boxes 320 and345 (FIG. 3) may be performed using weightings that are applied based onanalysis of the candidate zero crossings and the frequency coefficientsthat neighbor them. Thus, the counting may be performed as weightedsummations where individual candidate zero crossings are givenrelatively high or relatively low weights based on the outcome of theseadditional analyses.

FIG. 6 illustrates a method 600 of estimating source resolution of avideo sequence according to an embodiment of the present disclosure. Themethod 600 may estimate a source resolution of each frame of the videosequence (box 610) and determine whether a source resolution of theframe is below a predetermined limit (box 620). Estimation of the sourceresolution may occur as discussed in FIG. 3. If the estimated sourceresolution is lower than the predetermined limit, the method 600 mayincrement a count of upsampled frames detected for the video sequence(box 630). The operations of boxes 610-630 may be repeated for eachframe of the input video sequence.

Once all frames of the video sequence have been processed, the method600 may determine whether the count of upsampled frames exceeds athreshold (box 640). If so, the method 600 may cause the input videosequence to be rejected (box 650). If not, then the method 600 may causethe input video sequence to be admitted (box 660).

The method 600 finds application in a distribution server 120 (FIG. 1)to determine whether input videos should be admitted to the distributionsystem or rejected. Thus, when a distribution server 120 receives aninput video from an authoring source, it may perform the methods of FIG.3 and/or FIG. 6 to estimate whether the input video has a native sourcesize that is different than the size of the input video as it ispresented to the distribution server 120. If the distribution server 120estimates that the native source size of the input video is smaller thana required size, the distribution server 120 may reject the input videofrom being admitted to the media delivery system 100.

In an embodiment, rather than performing the method on every frame froma video sequence, the resolution estimation performed in box 610 may beperformed on a sub-set of frames from the video sequence. For example,the resolution estimation may be performed at a lower frame rate thanthe sequence's native frame rate, for example, on every fourth or fifthframe from the video sequence.

In a further embodiment, the number of frames on which the resolutionestimation is performed may vary dynamically based on frame content. Forexample, frames may be selected (or de-selected) from resolutionestimation based on variation in frame content as compared toneighboring frames. Thus, when processing a frame Fn, the method 600 maycompare content of frame Fn to content of a previous frame Fn−1 on apixel-by-pixel basis and generate an overall frame difference value ΔFnfrom an aggregation of the pixel differences. The method 600 may comparethe frame difference value ΔFn to a threshold TH to determine whetherresolution estimation should be performed. If the frame difference valueis lower than the threshold, then resolution estimation may be skippedbut, if the frame difference value is higher than the threshold, thenthe resolution estimation may be performed. In an embodiment, thethreshold may be content-adaptive. For example, the threshold may bedeveloped from statistics of the video sequence such as the mean andvariance of frame differences across a one-second window of video inwhich frames Fn and Fn−1 appear.

In some applications, the media delivery system 100 may operate as adistributor of produced audio-visual content including movies,television programming, and other production content. The media deliverysystem 100 may perform its analyses in conjunction with other processesof the distribution server 120 that parse input video into constituentparts. For example, a distribution server 120 may perform processes torecognize a portion of a movie representing production credits anddistinguish them from other parts representing narrative content. Inanother embodiment, the distribution server 120 may perform processes todistinguish scenes within the narrative content from each other. In suchembodiments, the distribution server 120 may perform the operations ofFIGS. 3 and/or 6 on each partition of the input video that thedistribution server 120 recognizes. It may apply different thresholds(box 640) to the different partitions. For example, the threshold may beunlimited for a partition representing movie credits but be set to 10%of the narrative portion of the movie. Similarly, the threshold may beset so that a violation of a given scene occurs if 10% of the scenecontains upsampled content and the video is rejected in its entirety if10% of the number of scenes is in violation. In practice, threshold(s)may be defined in whatever way may be convenient for operators of themedia delivery system 100.

The foregoing discussion has described operation of the embodiments ofthe present disclosure in the context of a media delivery system.Commonly, these components are provided as electronic devices, such as anetwork of coordinated servers. Media delivery systems can be embodiedin integrated circuits, such as application specific integratedcircuits, field programmable gate arrays and/or digital signalprocessors. Alternatively, they can be embodied in computer programsthat execute on personal computers, notebook computers, tabletcomputers, smartphones. Such computer programs typically are stored inphysical storage media such as electronic-, magnetic- and/oroptically-based storage devices, where they are read to a processor andexecuted. And, of course, these components may be provided as hybridsystems that distribute functionality across dedicated hardwarecomponents and programmed general-purpose processors, as desired.

For example, the techniques described herein may be performed by acentral processor of a computer system that serves as the mediadistribution system. FIG. 7 illustrates an exemplary computer system 700that may perform such techniques. The computer system 700 may include acentral processor 710, a memory 720, a coder 730, and a transceiver 740provided in communication with one another.

The central processor 710 may read and execute various programinstructions stored in the memory 720 that define an operating system722 of the system 700 and various applications 724.1-724.N. As itexecutes those program instructions, the central processor 710 may read,from the memory 720, which may be coded for transmission. In anembodiment, rather than provide a hardware-based coder 740, the centralprocessor 710 may execute a program 726 that operates as a coder.

As indicated, the memory 720 may store program instructions that, whenexecuted, cause the processor to perform the techniques describedhereinabove, such as the operations described in FIGS. 3 and 6. Thememory 720 may store the program instructions on electrical-, magnetic-and/or optically-based storage media.

The coder, whether provided as a hardware-based coder 730 or asoftware-based coder 726, may perform operations to compress ortranscode input videos for delivery to client devices 110 (FIG. 1). Aspart of its operation, the coder 730/726 may code input video dataaccording to a governing coding protocol such as ITU-T H.265, H.264 or apredecessor standard.

The transceiver 740 may represent a communication system to transmitvideos to client devices.

The foregoing description has been presented for purposes ofillustration and description. It is not exhaustive and does not limitembodiments of the disclosure to the precise forms disclosed.Modifications and variations are possible in light of the aboveteachings or may be acquired from the practicing embodiments consistentwith the disclosure. Unless described otherwise herein, any of themethods may be practiced in any combination.

We claim:
 1. A method comprising: converting an input image to a domainof frequency coefficients, each coefficient representing content of theinput image as a respective pair of frequencies each extending in one oftwo dimensions, for each set of frequency coefficients having a commonfrequency in a first dimension considered in order by frequency in asecond dimension, identifying zero crossings among the set of frequencycoefficients, counting the zero crossings from among the sets at eachfrequency in the second dimension, and estimating whether the inputimage was upsampled from the count of zero crossings.
 2. The method ofclaim 1, wherein the frequency coefficients are organized as an array ofthe coefficients having columns and rows, wherein the coefficients in acommon row have the common frequency in the first dimension and thecoefficients along each common row represent increasing frequencies inthe second dimension, and the identifying zero crossing occurs alongeach row, and the counting of zero crossings occurs at columnarpositions among the array.
 3. The method of claim 1, wherein thefrequency coefficients are organized as an array of the coefficientshaving columns and rows, wherein the coefficients in a common columnhave the common frequency in the first dimension and the coefficientsalong each common column represent increasing frequencies in the seconddimension, and the identifying zero crossing occurs along each column,and the counting of zero crossings occurs at row positions among thearray.
 4. The method of claim 1, wherein the zero crossings areidentified from frequency coefficients having a zero value.
 5. Themethod of claim 1, wherein the zero crossings are identified from adetermination that magnitudes of a predetermined number of frequencycoefficients on one side of a candidate zero crossing match magnitudesof another predetermined number of frequency coefficients at counterpartlocations on another side of the candidate zero crossing.
 6. The methodof claim 1, wherein the zero crossings are identified from adetermination that signs of a predetermined number of frequencycoefficients on one side of a candidate zero crossing are opposed tosigns of another predetermined number of frequency coefficients atcounterpart locations on another side of the candidate zero crossing. 7.A method comprising: estimating a native size of frames from a videosequence by, for each of a plurality of frames from the video sequence:converting the respective frame to a domain of frequency coefficients,each coefficient representing content of the input frame as a respectivepair of frequencies each extending in one of two dimensions; for eachconverted frame: for each set of frequency coefficients having a commonfrequency in a first dimension considered in order by frequency in asecond dimension, identifying zero crossings among the set of frequencycoefficients, counting the zero crossings from among the sets at eachfrequency in the second dimension, and estimating whether the inputframe was upsampled from the count of zero crossings; and when thenumber of input frames that are estimated as being upsampled exceed apredetermined value, rejecting the video sequence.
 8. The method ofclaim 7, wherein the video sequence is a partition of a media item. 9.The method of claim 7, further comprising, prior to the estimating thenative size, detecting scene changes from a media item, wherein thevideo sequence is a scene of the media item.
 10. The method of claim 7,further comprising, prior to the estimating the native size,partitioning a media item into partitions, and performing the estimatingthe native size for each partition of the media item, wherein thepredetermined value varies for different partitions.
 11. The method ofclaim 7, wherein: the frequency coefficients are organized as an arrayof the coefficients having columns and rows, wherein the coefficients ina common row have the common frequency in the first dimension and thecoefficients along each common row represent increasing frequencies inthe second dimension, and the identifying zero crossing occurs alongeach row, and the counting of zero crossings occurs at columnarpositions among the array.
 12. The method of claim 7, wherein thefrequency coefficients are organized as an array of the coefficientshaving columns and rows, wherein the coefficients in a common columnhave the common frequency in the first dimension and the coefficientsalong each common column represent increasing frequencies in the seconddimension, and the identifying zero crossing occurs along each column,and the counting of zero crossings occurs at row positions among thearray.
 13. The method of claim 7, wherein the zero crossings areidentified from frequency coefficients having a zero value.
 14. Themethod of claim 7, wherein the plurality of frames are selected from thevideo sequence at a rate lower than a native frame rate of the videosequence.
 15. The method of claim 7, wherein the plurality of frames areselected from the video sequence based on a comparison of each frame'scontent with their neighbor frames.
 16. The method of claim 7, whereinthe zero crossings are identified from a determination that magnitudesof a predetermined number of frequency coefficients on one side of acandidate zero crossing match magnitudes of another predetermined numberof frequency coefficients at counterpart locations on another side ofthe candidate zero crossing.
 17. The method of claim 7, wherein the zerocrossings are identified from a determination that signs of apredetermined number of frequency coefficients on one side of acandidate zero crossing are opposed to signs of another predeterminednumber of frequency coefficients at counterpart locations on anotherside of the candidate zero crossing.
 18. A media distribution system,comprising: a server to selectively admit and reject input videos basedon an estimation of native sizes of the input videos performed,respectively, on analysis of frequency domain representations of imageinformation of the input video, and a storage device to store admittedinput videos.
 19. The media distribution system of claim 18, wherein,for one of the input videos, the server: converts each frame of the oneinput video to a domain of frequency coefficients, each coefficientrepresenting content of the image information as a respective pair offrequencies each extending in one of two dimensions, for each set offrequency coefficients having a common frequency in a first dimensionconsidered in order by frequency in a second dimension, identifies zerocrossings among the set of frequency coefficients, counts the zerocrossings from among the sets at each frequency in the second dimension,and estimates whether the input video was upsampled from the count ofzero crossings.
 20. The media distribution system of claim 18, whereinfor one of the input videos, the server: prior to the estimating thenative size detects scene change(s) from the one input video, detectsscene changes in the one input video, estimates the native size of theinput video on a scene-by-scene basis, and rejects the one input videowhen the number of scenes that are estimated as having upsampled contentexceeds a predetermined value.
 21. The media distribution system ofclaim 18, wherein for one of the input videos, the server: partitionsthe one input video into partitions, and estimates the native size ofthe input video on a partition-by-partition basis, and rejects the oneinput video when the number frames in each partition that are estimatedas having upsampled content exceeds respective predetermined values,wherein the predetermined value varies for different partitions.
 22. Acomputer readable medium storing program instructions that, whenexecuted by a processing device, causes the device to: estimate a nativesize of frames from a video sequence by, for each of a plurality offrames from the video sequence: converting each frame to a domain offrequency coefficients, each coefficient representing content of therespective frame as a respective pair of frequencies each extending inone of two dimensions, for each converted frame: for each set offrequency coefficients having a common frequency in a first dimensionconsidered in order by frequency in a second dimension, identifying zerocrossings among the set of frequency coefficients, counting the zerocrossings from among the sets at each frequency in the second dimension,and estimating whether the input frame was upsampled from the count ofzero crossings; and when the number of selected frames that areestimated as being upsampled exceeds a predetermined value, reject thevideo sequence.